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Next Level Python in Data Science (Intermediate) | Numpy, Pandas, Spark, TensorFlow & More (5 days)

Length
5 days
Length
5 days
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Course description

Next Level Python in Data Science (Intermediate) | Numpy, Pandas, Spark, TensorFlow & More (5 days)

Next Level Python in Data Science covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, frameworks like TensorFlow and Spark, as well as visualization tools like matplotlib, PIL, and Seaborn.

This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge. Topics are introductory in nature, but are covered in-depth, geared for experienced students.

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Who should attend?

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. Attending students are required to have a background in basic Python development skills.

Training content

Session: Python for Data Science

Lesson: Python Review (Optional)

  • Python Language
  • Essential Syntax
  • Lists, Sets, Dictionaries, and Comprehensions
  • Functions
  • Classes, Modules, and imports
  • Exceptions

Lesson: iPython

  • iPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization
  • Web Notebooks (Jupyter)

Lesson: Numpy

  • Numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

Lesson: Scipy

  • What can scipy do?
  • Most useful functions
  • Curve fitting
  • Modeling
  • Data visualization
  • Statistics

Lesson: A tour of scipy subpackages

  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

Lesson: Pandas

  • Pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

Lesson: Matplotlib

  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

Lesson: The Python Imaging Library (PIL)

  • PIL overview
  • Core image library
  • Image processing
  • Displaying images

Lesson: Seaborn

  • Seaborn overview
  • Bivariate and univariate plots
  • Visualizing Linear Regressions
  • Visualizing Data Matrices
  • Working with Time Series data

Lesson: SciKit-Learn Machine Learning Essentials

  • SciKit overview
  • SciKit-Learn overview
  • Algorithms Overview
  • Classification, Regression, Clustering, and Dimensionality Reduction
  • SciKit Demo

Lesson: TensorFlow Overview

  • TensorFlow overview
  • Keras
  • Getting Started with TensorFlow

Session: Python on Spark

Lesson: PySpark Overview

  • Python and Spark
  • SciKit-Learn vs. Spark MLlib
  • Python at Scale
  • PySpark Demo

Lesson: RDDs and DataFrames

  • DataFrames and Resilient Distributed Datasets (RDDs)
  • Partitions
  • Adding variables to a DataFrame
  • DataFrame Types
  • DataFrame Operations
  • Dependent vs. Independent variables
  • Map/Reduce with DataFrames

Lesson: Spark SQL

  • Spark SQL Overview
  • Data stores: HDFS, Cassandra, HBase, Hive, and S3
  • Table Definitions
  • Queries

Lesson: Spark MLib

  • MLib overview
  • MLib Algorithms Overview
  • Classification Algorithms
  • Regression Algorithms
  • Decision Trees and forests
  • Recommendation with ALS
  • Clustering Algorithms
  • Machine Learning Pipelines
  • Linear Algebra (SVD, PCA)
  • Statistics in MLib

Lesson: Spark Streaming

  • Streaming overview
  • Integrating Spark SQL, MLlib, and Streaming

Costs

  • Price: $2,595.00
  • Discounted Price: $1,686.75

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Trivera Technologies LLC
7862 West Irlo Bronson Highway
STE 626
Kissimmee FL 34747

Trivera Technologies

Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...

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